What is sampling bias, and why is it a threat to a study?

• A. Sampling bias occurs when the sample is not representative; threatens generalizability and validity.
• B. Sampling bias occurs when data are collected from multiple sites.
• C. Sampling bias occurs when the sample size is too large to manage.
• D. Sampling bias occurs when researchers use random sampling improperly.

Answer: (A)

Sampling bias happens when the individuals chosen for a study don’t reflect the larger population you want to understand. When certain groups are overrepresented or underrepresented, the results can misrepresent how the whole population would respond, which threatens generalizability beyond the study and can distort conclusions about relationships or effects. For example, surveying only college students to draw conclusions about all adults misses the differences that might exist in other age groups.

The other options don’t define sampling bias. Collecting data from multiple sites isn’t, by itself, sampling bias—it can actually improve representativeness if sites are chosen thoughtfully. A sample that’s too large is not a bias issue, just a logistical one. Using random sampling improperly can produce biased results, but that describes a flaw in the procedure, not the fundamental definition of sampling bias itself.